Online association rule mining over fast data

Name: Online association rule mining over fast data
Author: Ölmezoğulları, Erdi, Arı, İsmail

İsim	Online association rule mining over fast data
Yazar	Ölmezoğulları, Erdi, Arı, İsmail
Basım Tarihi:	2013
Basım Yeri	- IEEE
Konu	Data mining, Music, Social networking (online)
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-0-7695-5006-0
Kayıt Numarası	27da04ec-5ee0-438f-a3f1-be488bc2965e
Lokasyon	Computer Science
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	To extract useful and actionable information in real-time, the information technology (IT) world is coping with big data problems today. In this paper, we present implementation details and performance results of ReCEPtor, our system for "online" Association Rule Mining (ARM) over big and fast data streams. Specifically, we added Apriori and two different FP-Growth algorithms inside Esper Complex Event Processing (CEP) engine and compared their performances using LastFM social music site data. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, and (3) much sooner (lower latency) than offline rule mining. In addition, we have found many interesting and realistic musical preference rules such as "George HarrisonàBeatles". We demonstrate a sustained rate of ~15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.
DOI	10.1109/BigData.Congress.2013.77

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Online association rule mining over fast data

Yazar Ölmezoğulları, Erdi, Arı, İsmail

Basım Tarihi 2013

Basım Yeri - IEEE

Konu Data mining, Music, Social networking (online)

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-0-7695-5006-0

Kayıt Numarası 27da04ec-5ee0-438f-a3f1-be488bc2965e

Lokasyon Computer Science

Tarih 2013

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin To extract useful and actionable information in real-time, the information technology (IT) world is coping with big data problems today. In this paper, we present implementation details and performance results of ReCEPtor, our system for "online" Association Rule Mining (ARM) over big and fast data streams. Specifically, we added Apriori and two different FP-Growth algorithms inside Esper Complex Event Processing (CEP) engine and compared their performances using LastFM social music site data. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, and (3) much sooner (lower latency) than offline rule mining. In addition, we have found many interesting and realistic musical preference rules such as "George HarrisonàBeatles". We demonstrate a sustained rate of ~15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.

DOI 10.1109/BigData.Congress.2013.77